1. Field of the Invention
The present invention relates to an apparatus and method for generating a coefficient, an apparatus and method for generating a class configuration, an informational signal processing apparatus, and programs for performing these methods. More specifically, it relates to an apparatus and method for generating a coefficient and the like suitable for being well applied to an apparatus for converting a standard TV signal (SD signal) into a high-resolution signal (HD signal) and the like.
2. Description of Related Art
In recent years, a variety of technologies have been proposed for improving a resolution or a sampling frequency of an image or audio signal. For example, it is known that in a case where a standard TV signal suited to a standard or low resolution is upgraded to a high-resolution signal, a so-called HDTV signal or where it undergoes sub-sample interpolation, conversion processing accompanied by class categorization gives a better result in performance than an approach by means of conventional linear interpolation.
According to this conversion processing accompanied by the class categorization, for example, in the case of converting a standard TV signal (SD signal) suited to a standard or low resolution into a high-resolution signal (HD signal), a class to which pixel data of a target position in the HD signal belongs is detected from a predetermined class configuration, so that using coefficient data for an estimation equation that corresponds to this class, the pixel data of the target position in the HD signal is generated from multiple items of pixel data of the SD signal based on this estimation equation. The coefficient data for the estimation equation used in this conversion processing accompanied by the class categorization is determined by performing learning such as least-squares method beforehand for each class.
However, to perform this conversion processing accompanied by class categorization, a class configuration (a combination of features) required to perform class categorization must be determined. Although generally the performance becomes better as the features are used more, an amount of the coefficient data or coefficient seed data which is coefficient data of a generation equation for generating this coefficient data may become enormous, or the calculation therefor may involve an immense amount of time. To solve this problem, it is important to determine an appropriate class configuration.
To determine the class configuration, it has conventionally been necessary to consider a few class configuration candidates obtained through human experiences in the past, perform learning separately for each class configuration, and select a seemingly best one of the class configurations based on a result of the leaning. Therefore, the human experiences are always relied on and the learning is always repeated from the beginning for each time the class configuration is changed, thus resulting in enormous time required for that.
It is an object of the present invention to efficiently generate coefficient data etc. for each class in an arbitrary class configuration by performing learning only once. It is another object of the present invention to obtain an optimal class configuration in short time without relying on human experiences. It is a further object of the present invention to convert a first informational signal into a second informational signal by performing conversion processing accompanied by class categorization by use of an optimal configuration.